r - Post-processing of rules from arules -

is there way how more 1 level of single variable gets used in single rule generated apriori in arules package?

consider following example:

df <- read.table(header = true, text = ' v1 v2 v3 d x  d x            d y         b d x        b d x        b d y e y e y e x b e y b e y b e x c d y ')  library(arules)     rules <- apriori(df,                   parameter = list(support= 0.001, confidence = 0.5, target = "rules"),                  appearance = list(rhs=c("v3=x"), default = 'lhs')) inspect(sort(rules, decreasing = true, = "confidence")) 

output>

  lhs       rhs      support confidence     lift 1 {v1=a,                                            v2=d} => {v3=x} 0.1538462  0.6666667 1.444444 2 {v1=b,                                            v2=d} => {v3=x} 0.1538462  0.6666667 1.444444 3 {v2=d} => {v3=x} 0.3076923  0.5714286 1.238095 4 {v1=a} => {v3=x} 0.2307692  0.5000000 1.083333 5 {v1=b} => {v3=x} 0.2307692  0.5000000 1.083333 

in example, helpful if rule {v1=a,b,v2=d}. other tools (e.g. lisp-miner) can generate rules more 1 level of variable used.

arules follows standard association rule mining literature , not aggregate items in way. itemsets either contain item or not. stuck 2 rules unless manually add artificial item v1=aorb.